//===- SparseTensorBase.td - Sparse tensor dialect base ----*- tablegen -*-===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//

#ifndef SPARSETENSOR_BASE
#define SPARSETENSOR_BASE

include "mlir/IR/OpBase.td"

def SparseTensor_Dialect : Dialect {
  let name = "sparse_tensor";
  let cppNamespace = "::mlir::sparse_tensor";
  let description = [{
    The `SparseTensor` dialect supports all the attributes, types,
    operations, and passes that are required to make sparse tensor
    types first class citizens within the MLIR compiler infrastructure.
    The dialect forms a bridge between high-level operations on sparse
    tensors types and lower-level operations on the actual sparse storage
    schemes consisting of positions, coordinates, and values. Lower-level
    support may consist of fully generated code or may be provided by
    means of a small sparse runtime support library.

    The concept of **treating sparsity as a property, not a tedious
    implementation detail**, by letting a **sparse compiler** generate
    sparse code automatically was pioneered for linear algebra by [Bik96]
    in MT1 (see https://www.aartbik.com/sparse.php) and formalized
    to tensor algebra by [Kjolstad17,Kjolstad20] in the Sparse Tensor
    Algebra Compiler (TACO) project (see http://tensor-compiler.org).

    The MLIR implementation [Biketal22] closely follows the "sparse
    iteration theory" that forms the foundation of TACO.  A rewriting
    rule is applied to each tensor expression in the Linalg dialect
    (MLIR's tensor index notation) where the sparsity of tensors is
    indicated using the per-level level-types (e.g., dense, compressed,
    singleton) together with a specification of the order on the levels
    (see [Chou18] for an in-depth discussions and possible extensions
    to these level-types).  Subsequently, a topologically sorted
    iteration graph, reflecting the required order on coordinates with
    respect to the levels of each tensor, is constructed to ensure
    that all tensors are visited in natural level-coordinate order.
    Next, iteration lattices are constructed for the tensor expression
    for every index in topological order.  Each iteration lattice point
    consists of a conjunction of tensor coordinates together with a tensor
    (sub)expression that needs to be evaluated for that conjunction.
    Within the lattice, iteration points are ordered according to
    the way coordinates are exhausted.  As such these iteration
    lattices drive actual sparse code generation, which consists of
    a relatively straightforward one-to-one mapping from iteration
    lattices to combinations of for-loops, while-loops, and if-statements.
    Sparse tensor outputs that materialize uninitialized are handled with
    direct insertions if all parallel loops are outermost or insertions
    that indirectly go through a 1-dimensional access pattern expansion
    (a.k.a. workspace) where feasible [Gustavson72,Bik96,Kjolstad19].

    * [Bik96] Aart J.C. Bik. Compiler Support for Sparse Matrix Computations.
    PhD thesis, Leiden University, May 1996.
    * [Biketal22] Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman,
    Nicolas Vasilache, Bixia Zheng, and Fredrik Kjolstad. Compiler Support
    for Sparse Tensor Computations in MLIR. ACM Transactions on Architecture
    and Code Optimization, June, 2022. See: https://dl.acm.org/doi/10.1145/3544559
    * [Chou18] Stephen Chou, Fredrik Berg Kjolstad, and Saman Amarasinghe.
    Format Abstraction for Sparse Tensor Algebra Compilers. Proceedings of
    the ACM on Programming Languages, October 2018.
    * [Chou20] Stephen Chou, Fredrik Berg Kjolstad, and Saman Amarasinghe.
    Automatic Generation of Efficient Sparse Tensor Format Conversion Routines.
    Proceedings of the 41st ACM SIGPLAN Conference on Programming Language
    Design and Implementation, June, 2020.
    * [Gustavson72] Fred G. Gustavson. Some basic techniques for solving
    sparse systems of linear equations. In Sparse Matrices and Their
    Applications, pages 41–52. Plenum Press, New York, 1972.
    * [Kjolstad17] Fredrik Berg Kjolstad, Shoaib Ashraf Kamil, Stephen Chou, David
    Lugato, and Saman Amarasinghe. The Tensor Algebra Compiler. Proceedings of
    the ACM on Programming Languages, October 2017.
    * [Kjolstad19] Fredrik Berg Kjolstad, Peter Ahrens, Shoaib Ashraf Kamil,
    and Saman Amarasinghe. Tensor Algebra Compilation with Workspaces,
    Proceedings of the IEEE/ACM International Symposium on Code Generation
    and Optimization, 2019.
    * [Kjolstad20] Fredrik Berg Kjolstad. Sparse Tensor Algebra Compilation.
    PhD thesis, MIT, February, 2020.
  }];

  let useDefaultAttributePrinterParser = 1;
  let useDefaultTypePrinterParser = 1;
}

#endif // SPARSETENSOR_BASE
